Overview

Brought to you by YData

Dataset statistics

Number of variables41
Number of observations198900
Missing cells98472
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory313.8 MiB
Average record size in memory1.6 KiB

Variable types

Text8
Numeric15
Categorical6
DateTime7
Boolean5

Alerts

Estimated Cost is highly overall correlated with Revised CostHigh correlation
Existing Construction Type is highly overall correlated with Existing Construction Type Description and 2 other fieldsHigh correlation
Existing Construction Type Description is highly overall correlated with Existing Construction Type and 2 other fieldsHigh correlation
Existing Units is highly overall correlated with Proposed UnitsHigh correlation
Neighborhoods - Analysis Boundaries is highly overall correlated with Supervisor District and 1 other fieldsHigh correlation
Number of Existing Stories is highly overall correlated with Number of Proposed StoriesHigh correlation
Number of Proposed Stories is highly overall correlated with Number of Existing StoriesHigh correlation
Permit Type is highly overall correlated with Permit Type Definition and 1 other fieldsHigh correlation
Permit Type Definition is highly overall correlated with Permit Type and 1 other fieldsHigh correlation
Proposed Construction Type is highly overall correlated with Existing Construction Type and 2 other fieldsHigh correlation
Proposed Construction Type Description is highly overall correlated with Existing Construction Type and 2 other fieldsHigh correlation
Proposed Units is highly overall correlated with Existing UnitsHigh correlation
Revised Cost is highly overall correlated with Estimated CostHigh correlation
Site Permit is highly overall correlated with Permit Type and 2 other fieldsHigh correlation
Structural Notification is highly overall correlated with Site PermitHigh correlation
Supervisor District is highly overall correlated with Neighborhoods - Analysis BoundariesHigh correlation
Zipcode is highly overall correlated with Neighborhoods - Analysis BoundariesHigh correlation
Permit Type Definition is highly imbalanced (79.7%)Imbalance
Street Suffix is highly imbalanced (70.1%)Imbalance
Current Status is highly imbalanced (60.4%)Imbalance
Structural Notification is highly imbalanced (78.2%)Imbalance
Voluntary Soft-Story Retrofit is highly imbalanced (99.8%)Imbalance
Fire Only Permit is highly imbalanced (54.8%)Imbalance
TIDF Compliance is highly imbalanced (> 99.9%)Imbalance
Site Permit is highly imbalanced (82.1%)Imbalance
Completed Date has 59463 (29.9%) missing valuesMissing
Plansets has 37309 (18.8%) missing valuesMissing
Unit is highly skewed (γ1 = 21.14443891)Skewed
Estimated Cost is highly skewed (γ1 = 89.27829995)Skewed
Revised Cost is highly skewed (γ1 = 100.9590765)Skewed
Plansets is highly skewed (γ1 = 400.8322084)Skewed
Existing Construction Type is highly skewed (γ1 = 39.8686473)Skewed
Record ID has unique valuesUnique
Unit has 169421 (85.2%) zerosZeros
Number of Existing Stories has 42784 (21.5%) zerosZeros
Number of Proposed Stories has 39374 (19.8%) zerosZeros
Existing Units has 29134 (14.6%) zerosZeros
Proposed Units has 28632 (14.4%) zerosZeros
Plansets has 63244 (31.8%) zerosZeros

Reproduction

Analysis started2024-08-30 23:17:27.217125
Analysis finished2024-08-30 23:20:12.909591
Duration2 minutes and 45.69 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Distinct181495
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Memory size14.4 MiB
2024-08-30T20:20:14.002570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.063273
Min length7

Characters and Unicode

Total characters2200485
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique167282 ?
Unique (%)84.1%

Sample

1st rowM788927
2nd row201305318356
3rd row201705106205
4th row201410279983
5th row201310280388
ValueCountFrequency (%)
201602179765 101
 
0.1%
201602179758 66
 
< 0.1%
201602179775 30
 
< 0.1%
201702239990 9
 
< 0.1%
201409166451 9
 
< 0.1%
201708165004 9
 
< 0.1%
201702099112 8
 
< 0.1%
201707061162 8
 
< 0.1%
201604285989 8
 
< 0.1%
201604074211 8
 
< 0.1%
Other values (181485) 198644
99.9%
2024-08-30T20:20:15.237467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 445620
20.3%
1 376235
17.1%
2 338882
15.4%
7 174079
 
7.9%
6 150943
 
6.9%
4 147666
 
6.7%
3 145527
 
6.6%
5 144929
 
6.6%
8 130425
 
5.9%
9 108916
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2200485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 445620
20.3%
1 376235
17.1%
2 338882
15.4%
7 174079
 
7.9%
6 150943
 
6.9%
4 147666
 
6.7%
3 145527
 
6.6%
5 144929
 
6.6%
8 130425
 
5.9%
9 108916
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2200485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 445620
20.3%
1 376235
17.1%
2 338882
15.4%
7 174079
 
7.9%
6 150943
 
6.9%
4 147666
 
6.7%
3 145527
 
6.6%
5 144929
 
6.6%
8 130425
 
5.9%
9 108916
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2200485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 445620
20.3%
1 376235
17.1%
2 338882
15.4%
7 174079
 
7.9%
6 150943
 
6.9%
4 147666
 
6.7%
3 145527
 
6.6%
5 144929
 
6.6%
8 130425
 
5.9%
9 108916
 
4.9%

Permit Type
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5223228
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-30T20:20:15.541482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median8
Q38
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4574508
Coefficient of variation (CV)0.19375011
Kurtosis6.0363783
Mean7.5223228
Median Absolute Deviation (MAD)0
Skewness-2.7990091
Sum1496190
Variance2.124163
MonotonicityNot monotonic
2024-08-30T20:20:15.865690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
8 178844
89.9%
3 14663
 
7.4%
4 2892
 
1.5%
2 950
 
0.5%
6 600
 
0.3%
7 511
 
0.3%
1 349
 
0.2%
5 91
 
< 0.1%
ValueCountFrequency (%)
1 349
 
0.2%
2 950
 
0.5%
3 14663
 
7.4%
4 2892
 
1.5%
5 91
 
< 0.1%
6 600
 
0.3%
7 511
 
0.3%
8 178844
89.9%
ValueCountFrequency (%)
8 178844
89.9%
7 511
 
0.3%
6 600
 
0.3%
5 91
 
< 0.1%
4 2892
 
1.5%
3 14663
 
7.4%
2 950
 
0.5%
1 349
 
0.2%

Permit Type Definition
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
otc alterations permit
178844 
additions alterations or repairs
 
14663
sign - erect
 
2892
new construction wood frame
 
950
demolitions
 
600
Other values (3)
 
951

Length

Max length35
Median length22
Mean length22.572785
Min length11

Characters and Unicode

Total characters4489727
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowotc alterations permit
2nd rowotc alterations permit
3rd rowotc alterations permit
4th rowotc alterations permit
5th rowotc alterations permit

Common Values

ValueCountFrequency (%)
otc alterations permit 178844
89.9%
additions alterations or repairs 14663
 
7.4%
sign - erect 2892
 
1.5%
new construction wood frame 950
 
0.5%
demolitions 600
 
0.3%
wall or painted sign 511
 
0.3%
new construction 349
 
0.2%
grade or quarry or fill or excavate 91
 
< 0.1%

Length

2024-08-30T20:20:16.300525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-30T20:20:16.725422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
alterations 193507
31.6%
otc 178844
29.2%
permit 178844
29.2%
or 15447
 
2.5%
additions 14663
 
2.4%
repairs 14663
 
2.4%
sign 3403
 
0.6%
2892
 
0.5%
erect 2892
 
0.5%
construction 1299
 
0.2%
Other values (10) 5185
 
0.8%

Most occurring characters

ValueCountFrequency (%)
t 766057
17.1%
i 422844
9.4%
r 422538
9.4%
a 418676
9.3%
412739
9.2%
o 408159
9.1%
e 396431
8.8%
s 228135
 
5.1%
n 216581
 
4.8%
l 195311
 
4.4%
Other values (13) 602256
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4489727
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 766057
17.1%
i 422844
9.4%
r 422538
9.4%
a 418676
9.3%
412739
9.2%
o 408159
9.1%
e 396431
8.8%
s 228135
 
5.1%
n 216581
 
4.8%
l 195311
 
4.4%
Other values (13) 602256
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4489727
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 766057
17.1%
i 422844
9.4%
r 422538
9.4%
a 418676
9.3%
412739
9.2%
o 408159
9.1%
e 396431
8.8%
s 228135
 
5.1%
n 216581
 
4.8%
l 195311
 
4.4%
Other values (13) 602256
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4489727
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 766057
17.1%
i 422844
9.4%
r 422538
9.4%
a 418676
9.3%
412739
9.2%
o 408159
9.1%
e 396431
8.8%
s 228135
 
5.1%
n 216581
 
4.8%
l 195311
 
4.4%
Other values (13) 602256
13.4%
Distinct1292
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Minimum2012-03-28 00:00:00
Maximum2018-12-02 00:00:00
2024-08-30T20:20:17.147287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:20:17.558519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Block
Text

Distinct4896
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size13.1 MiB
2024-08-30T20:20:18.799461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0363901
Min length3

Characters and Unicode

Total characters802838
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)0.1%

Sample

1st row0215
2nd row1810
3rd row5700
4th row0661
5th row3642
ValueCountFrequency (%)
3708 1195
 
0.6%
3735 750
 
0.4%
7331 680
 
0.3%
0289 640
 
0.3%
3709 584
 
0.3%
3717 578
 
0.3%
3707 576
 
0.3%
3721 567
 
0.3%
3706 561
 
0.3%
0259 554
 
0.3%
Other values (4886) 192215
96.6%
2024-08-30T20:20:20.327970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 124324
15.5%
3 97522
12.1%
1 96591
12.0%
2 85369
10.6%
5 80945
10.1%
6 77731
9.7%
7 73540
9.2%
4 60015
7.5%
8 50674
6.3%
9 48897
 
6.1%
Other values (9) 7230
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 802838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 124324
15.5%
3 97522
12.1%
1 96591
12.0%
2 85369
10.6%
5 80945
10.1%
6 77731
9.7%
7 73540
9.2%
4 60015
7.5%
8 50674
6.3%
9 48897
 
6.1%
Other values (9) 7230
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 802838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 124324
15.5%
3 97522
12.1%
1 96591
12.0%
2 85369
10.6%
5 80945
10.1%
6 77731
9.7%
7 73540
9.2%
4 60015
7.5%
8 50674
6.3%
9 48897
 
6.1%
Other values (9) 7230
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 802838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 124324
15.5%
3 97522
12.1%
1 96591
12.0%
2 85369
10.6%
5 80945
10.1%
6 77731
9.7%
7 73540
9.2%
4 60015
7.5%
8 50674
6.3%
9 48897
 
6.1%
Other values (9) 7230
 
0.9%

Lot
Text

Distinct1055
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
2024-08-30T20:20:21.304156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0798793
Min length3

Characters and Unicode

Total characters612588
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique180 ?
Unique (%)0.1%

Sample

1st row001
2nd row017A
3rd row027
4th row005
5th row051A
ValueCountFrequency (%)
001 10114
 
5.1%
007 5317
 
2.7%
002 5183
 
2.6%
003 5042
 
2.5%
006 4835
 
2.4%
008 4773
 
2.4%
009 4590
 
2.3%
005 4549
 
2.3%
004 4384
 
2.2%
011 4238
 
2.1%
Other values (1045) 145875
73.3%
2024-08-30T20:20:22.653800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 266808
43.6%
1 80408
 
13.1%
2 59432
 
9.7%
3 43025
 
7.0%
4 33217
 
5.4%
5 26862
 
4.4%
6 25006
 
4.1%
7 22210
 
3.6%
8 20304
 
3.3%
9 19441
 
3.2%
Other values (26) 15875
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 612588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 266808
43.6%
1 80408
 
13.1%
2 59432
 
9.7%
3 43025
 
7.0%
4 33217
 
5.4%
5 26862
 
4.4%
6 25006
 
4.1%
7 22210
 
3.6%
8 20304
 
3.3%
9 19441
 
3.2%
Other values (26) 15875
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 612588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 266808
43.6%
1 80408
 
13.1%
2 59432
 
9.7%
3 43025
 
7.0%
4 33217
 
5.4%
5 26862
 
4.4%
6 25006
 
4.1%
7 22210
 
3.6%
8 20304
 
3.3%
9 19441
 
3.2%
Other values (26) 15875
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 612588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 266808
43.6%
1 80408
 
13.1%
2 59432
 
9.7%
3 43025
 
7.0%
4 33217
 
5.4%
5 26862
 
4.4%
6 25006
 
4.1%
7 22210
 
3.6%
8 20304
 
3.3%
9 19441
 
3.2%
Other values (26) 15875
 
2.6%

Street Number
Real number (ℝ)

Distinct5099
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13353916
Minimum0
Maximum1
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-30T20:20:23.052627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0038095238
Q10.02797619
median0.08452381
Q30.20238095
95-th percentile0.41440476
Maximum1
Range1
Interquartile range (IQR)0.17440476

Descriptive statistics

Standard deviation0.13521059
Coefficient of variation (CV)1.0125164
Kurtosis2.134618
Mean0.13353916
Median Absolute Deviation (MAD)0.069047619
Skewness1.4268475
Sum26560.939
Variance0.018281903
MonotonicityNot monotonic
2024-08-30T20:20:23.673823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.000119047619 2394
 
1.2%
0.01202380952 1153
 
0.6%
0.0119047619 1143
 
0.6%
0.005952380952 1103
 
0.6%
0.02392857143 1026
 
0.5%
0.06607142857 994
 
0.5%
0.0002380952381 814
 
0.4%
0.006547619048 734
 
0.4%
0.04166666667 705
 
0.4%
0.01785714286 672
 
0.3%
Other values (5089) 188162
94.6%
ValueCountFrequency (%)
0 4
 
< 0.1%
0.000119047619 2394
1.2%
0.0002380952381 814
 
0.4%
0.0003571428571 309
 
0.2%
0.0004761904762 255
 
0.1%
0.0005952380952 194
 
0.1%
0.0007142857143 113
 
0.1%
0.0008333333333 132
 
0.1%
0.0009523809524 281
 
0.1%
0.001071428571 128
 
0.1%
ValueCountFrequency (%)
1 3
< 0.1%
0.9917857143 1
 
< 0.1%
0.9910714286 5
< 0.1%
0.9904761905 1
 
< 0.1%
0.9880952381 1
 
< 0.1%
0.981547619 1
 
< 0.1%
0.9798809524 1
 
< 0.1%
0.9795238095 1
 
< 0.1%
0.9788095238 7
< 0.1%
0.978452381 1
 
< 0.1%
Distinct1704
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
2024-08-30T20:20:24.479591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length6.5097185
Min length3

Characters and Unicode

Total characters1294783
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)0.1%

Sample

1st rowJones
2nd row43Rd
3rd rowPrentiss
4th rowBush
5th rowCapp
ValueCountFrequency (%)
market 5443
 
2.6%
california 4587
 
2.2%
mission 4324
 
2.0%
montgomery 2933
 
1.4%
geary 1966
 
0.9%
20th 1859
 
0.9%
03rd 1819
 
0.9%
folsom 1776
 
0.8%
van 1697
 
0.8%
pine 1677
 
0.8%
Other values (1705) 184669
86.8%
2024-08-30T20:20:25.764178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 120375
 
9.3%
e 105510
 
8.1%
o 94415
 
7.3%
r 92518
 
7.1%
n 88438
 
6.8%
i 67764
 
5.2%
t 63534
 
4.9%
l 58445
 
4.5%
s 54238
 
4.2%
h 47210
 
3.6%
Other values (54) 502336
38.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1294783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 120375
 
9.3%
e 105510
 
8.1%
o 94415
 
7.3%
r 92518
 
7.1%
n 88438
 
6.8%
i 67764
 
5.2%
t 63534
 
4.9%
l 58445
 
4.5%
s 54238
 
4.2%
h 47210
 
3.6%
Other values (54) 502336
38.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1294783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 120375
 
9.3%
e 105510
 
8.1%
o 94415
 
7.3%
r 92518
 
7.1%
n 88438
 
6.8%
i 67764
 
5.2%
t 63534
 
4.9%
l 58445
 
4.5%
s 54238
 
4.2%
h 47210
 
3.6%
Other values (54) 502336
38.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1294783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 120375
 
9.3%
e 105510
 
8.1%
o 94415
 
7.3%
r 92518
 
7.1%
n 88438
 
6.8%
i 67764
 
5.2%
t 63534
 
4.9%
l 58445
 
4.5%
s 54238
 
4.2%
h 47210
 
3.6%
Other values (54) 502336
38.8%

Street Suffix
Categorical

IMBALANCE 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
St
141126 
Av
43219 
Bl
 
3555
Wy
 
3540
Dr
 
3267
Other values (16)
 
4193

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters397800
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSt
2nd rowAv
3rd rowSt
4th rowSt
5th rowSt

Common Values

ValueCountFrequency (%)
St 141126
71.0%
Av 43219
 
21.7%
Bl 3555
 
1.8%
Wy 3540
 
1.8%
Dr 3267
 
1.6%
Tr 1466
 
0.7%
Ct 667
 
0.3%
Pl 538
 
0.3%
Rd 389
 
0.2%
Ln 354
 
0.2%
Other values (11) 779
 
0.4%

Length

2024-08-30T20:20:26.077509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st 141126
71.0%
av 43219
 
21.7%
bl 3555
 
1.8%
wy 3540
 
1.8%
dr 3267
 
1.6%
tr 1466
 
0.7%
ct 667
 
0.3%
pl 538
 
0.3%
rd 389
 
0.2%
ln 354
 
0.2%
Other values (11) 779
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 141793
35.6%
S 141130
35.5%
A 43302
 
10.9%
v 43219
 
10.9%
r 4830
 
1.2%
l 4177
 
1.1%
y 3780
 
1.0%
B 3555
 
0.9%
W 3549
 
0.9%
D 3267
 
0.8%
Other values (13) 5198
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 397800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 141793
35.6%
S 141130
35.5%
A 43302
 
10.9%
v 43219
 
10.9%
r 4830
 
1.2%
l 4177
 
1.1%
y 3780
 
1.0%
B 3555
 
0.9%
W 3549
 
0.9%
D 3267
 
0.8%
Other values (13) 5198
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 397800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 141793
35.6%
S 141130
35.5%
A 43302
 
10.9%
v 43219
 
10.9%
r 4830
 
1.2%
l 4177
 
1.1%
y 3780
 
1.0%
B 3555
 
0.9%
W 3549
 
0.9%
D 3267
 
0.8%
Other values (13) 5198
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 397800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 141793
35.6%
S 141130
35.5%
A 43302
 
10.9%
v 43219
 
10.9%
r 4830
 
1.2%
l 4177
 
1.1%
y 3780
 
1.0%
B 3555
 
0.9%
W 3549
 
0.9%
D 3267
 
0.8%
Other values (13) 5198
 
1.3%

Unit
Real number (ℝ)

SKEWED  ZEROS 

Distinct661
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0019625735
Minimum0
Maximum1
Zeros169421
Zeros (%)85.2%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-30T20:20:26.465376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.00016652789
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.021483994
Coefficient of variation (CV)10.946848
Kurtosis621.62611
Mean0.0019625735
Median Absolute Deviation (MAD)0
Skewness21.144439
Sum390.35587
Variance0.00046156201
MonotonicityNot monotonic
2024-08-30T20:20:26.835004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 169421
85.2%
0.0001665278934 21410
 
10.8%
0.0003330557868 1312
 
0.7%
0.0004995836803 549
 
0.3%
0.0006661115737 448
 
0.2%
0.01698584513 365
 
0.2%
0.0008326394671 288
 
0.1%
0.001165695254 214
 
0.1%
0.0009991673605 211
 
0.1%
0.03363863447 210
 
0.1%
Other values (651) 4472
 
2.2%
ValueCountFrequency (%)
0 169421
85.2%
0.0001665278934 21410
 
10.8%
0.0003330557868 1312
 
0.7%
0.0004995836803 549
 
0.3%
0.0006661115737 448
 
0.2%
0.0008326394671 288
 
0.1%
0.0009991673605 211
 
0.1%
0.001165695254 214
 
0.1%
0.001332223147 103
 
0.1%
0.001498751041 104
 
0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.9998334721 3
< 0.1%
0.9831806828 1
 
< 0.1%
0.9333888426 1
 
< 0.1%
0.9330557868 1
 
< 0.1%
0.901582015 1
 
< 0.1%
0.9014154871 1
 
< 0.1%
0.8849292256 1
 
< 0.1%
0.8847626978 1
 
< 0.1%
0.8837635304 1
 
< 0.1%
Distinct134273
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Memory size31.6 MiB
2024-08-30T20:20:27.737316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length300
Median length243
Mean length101.3254
Min length1

Characters and Unicode

Total characters20153622
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122040 ?
Unique (%)61.4%

Sample

1st rowstreet space
2nd rowremodel kitchen: replace countertop, cabinets, sink, stove, hardwood floor & lighting.
3rd rowreplacement of 4 windows; 2 located in lightwell and 2 at front. replaceing existing vinyl windows with new wood-clad windows. no horizontal mullion in living room window
4th rowtwo kitchens & two bathrooms remodel. replace kitchen cabinets, countertops & appliances in same location. replace tub & faucet & vanity & tile in same location in both units.
5th rowreroofing
ValueCountFrequency (%)
94912
 
3.0%
to 93336
 
2.9%
and 68890
 
2.2%
new 61782
 
1.9%
of 54700
 
1.7%
street 44271
 
1.4%
space 41098
 
1.3%
replace 36545
 
1.1%
in 36108
 
1.1%
for 35059
 
1.1%
Other values (109535) 2637168
82.3%
2024-08-30T20:20:29.140399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3042970
15.1%
e 1735498
 
8.6%
o 1280567
 
6.4%
r 1278068
 
6.3%
t 1208498
 
6.0%
n 1154212
 
5.7%
a 1149538
 
5.7%
i 1122733
 
5.6%
s 828668
 
4.1%
l 785515
 
3.9%
Other values (67) 6567355
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20153622
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3042970
15.1%
e 1735498
 
8.6%
o 1280567
 
6.4%
r 1278068
 
6.3%
t 1208498
 
6.0%
n 1154212
 
5.7%
a 1149538
 
5.7%
i 1122733
 
5.6%
s 828668
 
4.1%
l 785515
 
3.9%
Other values (67) 6567355
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20153622
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3042970
15.1%
e 1735498
 
8.6%
o 1280567
 
6.4%
r 1278068
 
6.3%
t 1208498
 
6.0%
n 1154212
 
5.7%
a 1149538
 
5.7%
i 1122733
 
5.6%
s 828668
 
4.1%
l 785515
 
3.9%
Other values (67) 6567355
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20153622
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3042970
15.1%
e 1735498
 
8.6%
o 1280567
 
6.4%
r 1278068
 
6.3%
t 1208498
 
6.0%
n 1154212
 
5.7%
a 1149538
 
5.7%
i 1122733
 
5.6%
s 828668
 
4.1%
l 785515
 
3.9%
Other values (67) 6567355
32.6%

Current Status
Categorical

IMBALANCE 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.7 MiB
complete
97077 
issued
83559 
filed
12043 
withdrawn
 
1754
cancelled
 
1536
Other values (9)
 
2931

Length

Max length11
Median length10
Mean length6.9923479
Min length5

Characters and Unicode

Total characters1390778
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowissued
2nd rowcomplete
3rd rowissued
4th rowcomplete
5th rowissued

Common Values

ValueCountFrequency (%)
complete 97077
48.8%
issued 83559
42.0%
filed 12043
 
6.1%
withdrawn 1754
 
0.9%
cancelled 1536
 
0.8%
expired 1370
 
0.7%
approved 733
 
0.4%
reinstated 563
 
0.3%
suspend 193
 
0.1%
revoked 50
 
< 0.1%
Other values (4) 22
 
< 0.1%

Length

2024-08-30T20:20:29.471816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
complete 97077
48.8%
issued 83559
42.0%
filed 12043
 
6.1%
withdrawn 1754
 
0.9%
cancelled 1536
 
0.8%
expired 1370
 
0.7%
approved 733
 
0.4%
reinstated 563
 
0.3%
suspend 193
 
0.1%
revoked 50
 
< 0.1%
Other values (4) 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 297744
21.4%
s 168069
12.1%
l 112212
 
8.1%
d 101805
 
7.3%
c 100183
 
7.2%
p 100132
 
7.2%
t 99959
 
7.2%
i 99293
 
7.1%
o 97864
 
7.0%
m 97079
 
7.0%
Other values (10) 116438
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1390778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 297744
21.4%
s 168069
12.1%
l 112212
 
8.1%
d 101805
 
7.3%
c 100183
 
7.2%
p 100132
 
7.2%
t 99959
 
7.2%
i 99293
 
7.1%
o 97864
 
7.0%
m 97079
 
7.0%
Other values (10) 116438
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1390778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 297744
21.4%
s 168069
12.1%
l 112212
 
8.1%
d 101805
 
7.3%
c 100183
 
7.2%
p 100132
 
7.2%
t 99959
 
7.2%
i 99293
 
7.1%
o 97864
 
7.0%
m 97079
 
7.0%
Other values (10) 116438
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1390778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 297744
21.4%
s 168069
12.1%
l 112212
 
8.1%
d 101805
 
7.3%
c 100183
 
7.2%
p 100132
 
7.2%
t 99959
 
7.2%
i 99293
 
7.1%
o 97864
 
7.0%
m 97079
 
7.0%
Other values (10) 116438
 
8.4%
Distinct1307
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Minimum2013-01-02 00:00:00
Maximum2019-02-03 00:00:00
2024-08-30T20:20:29.915433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:20:30.427059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1288
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Minimum2013-01-02 00:00:00
Maximum2018-12-02 00:00:00
2024-08-30T20:20:30.959920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:20:31.482626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1289
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Minimum2013-01-02 00:00:00
Maximum2018-12-02 00:00:00
2024-08-30T20:20:31.889933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:20:32.331484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Completed Date
Date

MISSING 

Distinct514
Distinct (%)0.4%
Missing59463
Missing (%)29.9%
Memory size3.0 MiB
Minimum1900-01-01 00:00:00
Maximum2018-02-12 00:00:00
2024-08-30T20:20:32.794898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:20:33.284720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1289
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Minimum1900-01-01 00:00:00
Maximum2018-12-02 00:00:00
2024-08-30T20:20:33.930140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:20:35.131713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Structural Notification
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
191978 
True
 
6922
ValueCountFrequency (%)
False 191978
96.5%
True 6922
 
3.5%
2024-08-30T20:20:35.502673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Number of Existing Stories
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.066624473
Minimum0
Maximum1
Zeros42784
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-30T20:20:35.818611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.025316456
median0.037974684
Q30.050632911
95-th percentile0.30379747
Maximum1
Range1
Interquartile range (IQR)0.025316456

Descriptive statistics

Standard deviation0.10269932
Coefficient of variation (CV)1.5414654
Kurtosis13.677226
Mean0.066624473
Median Absolute Deviation (MAD)0.012658228
Skewness3.5147885
Sum13251.608
Variance0.010547151
MonotonicityNot monotonic
2024-08-30T20:20:36.262249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03797468354 52769
26.5%
0.05063291139 45740
23.0%
0 42784
21.5%
0.06329113924 16055
 
8.1%
0.0253164557 8794
 
4.4%
0.07594936709 3767
 
1.9%
0.08860759494 3696
 
1.9%
0.1012658228 2358
 
1.2%
0.1139240506 1731
 
0.9%
0.1265822785 1264
 
0.6%
Other values (53) 19942
 
10.0%
ValueCountFrequency (%)
0 42784
21.5%
0.01265822785 442
 
0.2%
0.0253164557 8794
 
4.4%
0.03797468354 52769
26.5%
0.05063291139 45740
23.0%
0.06329113924 16055
 
8.1%
0.07594936709 3767
 
1.9%
0.08860759494 3696
 
1.9%
0.1012658228 2358
 
1.2%
0.1139240506 1731
 
0.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.8101265823 99
< 0.1%
0.7974683544 2
 
< 0.1%
0.7848101266 24
 
< 0.1%
0.7721518987 18
 
< 0.1%
0.746835443 93
< 0.1%
0.7215189873 4
 
< 0.1%
0.7088607595 14
 
< 0.1%
0.6962025316 7
 
< 0.1%
0.6835443038 12
 
< 0.1%

Number of Proposed Stories
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.068513024
Minimum0
Maximum1
Zeros39374
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-30T20:20:36.713142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.037974684
median0.037974684
Q30.063291139
95-th percentile0.30379747
Maximum1
Range1
Interquartile range (IQR)0.025316456

Descriptive statistics

Standard deviation0.10338426
Coefficient of variation (CV)1.5089723
Kurtosis13.559891
Mean0.068513024
Median Absolute Deviation (MAD)0.012658228
Skewness3.4995812
Sum13627.241
Variance0.010688305
MonotonicityNot monotonic
2024-08-30T20:20:37.184917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03797468354 51874
26.1%
0.05063291139 47248
23.8%
0 39374
19.8%
0.06329113924 17889
 
9.0%
0.0253164557 8638
 
4.3%
0.07594936709 4199
 
2.1%
0.08860759494 3988
 
2.0%
0.1012658228 2423
 
1.2%
0.1139240506 1802
 
0.9%
0.1265822785 1397
 
0.7%
Other values (53) 20068
 
10.1%
ValueCountFrequency (%)
0 39374
19.8%
0.01265822785 175
 
0.1%
0.0253164557 8638
 
4.3%
0.03797468354 51874
26.1%
0.05063291139 47248
23.8%
0.06329113924 17889
 
9.0%
0.07594936709 4199
 
2.1%
0.08860759494 3988
 
2.0%
0.1012658228 2423
 
1.2%
0.1139240506 1802
 
0.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.8101265823 113
0.1%
0.7974683544 4
 
< 0.1%
0.7848101266 24
 
< 0.1%
0.7721518987 19
 
< 0.1%
0.746835443 93
< 0.1%
0.7215189873 11
 
< 0.1%
0.7088607595 23
 
< 0.1%
0.6962025316 14
 
< 0.1%
0.6835443038 12
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
198865 
True
 
35
ValueCountFrequency (%)
False 198865
> 99.9%
True 35
 
< 0.1%
2024-08-30T20:20:37.554326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Fire Only Permit
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
180073 
True
18827 
ValueCountFrequency (%)
False 180073
90.5%
True 18827
 
9.5%
2024-08-30T20:20:37.857632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2232
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Minimum2013-07-10 00:00:00
Maximum2024-11-01 00:00:00
2024-08-30T20:20:38.224273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:20:38.692184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estimated Cost
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct11395
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00035124895
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-30T20:20:39.193954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.7846007 × 10-6
Q11.4867314 × 10-5
median5.5762651 × 10-5
Q30.00035132925
95-th percentile0.00037177207
Maximum1
Range1
Interquartile range (IQR)0.00033646194

Descriptive statistics

Standard deviation0.0060676363
Coefficient of variation (CV)17.274461
Kurtosis11025.015
Mean0.00035124895
Median Absolute Deviation (MAD)5.1676835 × 10-5
Skewness89.2783
Sum69.863416
Variance3.6816211 × 10-5
MonotonicityNot monotonic
2024-08-30T20:20:39.657755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0003513292513 55080
27.7%
1.858507176 × 10-56695
 
3.4%
9.290676999 × 10-66437
 
3.2%
3.717386127 × 10-55702
 
2.9%
2.787946651 × 10-54779
 
2.4%
5.576265078 × 10-53939
 
2.0%
4.646825602 × 10-53599
 
1.8%
5.572919096 × 10-63568
 
1.8%
9.294022981 × 10-53345
 
1.7%
3.714040145 × 10-63203
 
1.6%
Other values (11385) 102553
51.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
3.717757903 × 10-92
 
< 0.1%
5.576636854 × 10-93
 
< 0.1%
1.487103161 × 10-811
 
< 0.1%
3.345982112 × 10-85
 
< 0.1%
4.275421588 × 10-87
 
< 0.1%
5.204861064 × 10-83
 
< 0.1%
6.134300539 × 10-81
 
< 0.1%
7.063740015 × 10-81
 
< 0.1%
8.922618966 × 10-841
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.966617051 1
< 0.1%
0.7435515768 2
< 0.1%
0.6320188397 1
< 0.1%
0.505615071 1
< 0.1%
0.5018973131 1
< 0.1%
0.4442720656 1
< 0.1%
0.3903645761 1
< 0.1%
0.3253038128 1
< 0.1%
0.3104327811 2
< 0.1%

Revised Cost
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct12869
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00027286707
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-30T20:20:40.127494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.9218322 × 10-6
Q11.1531057 × 10-5
median4.9566931 × 10-5
Q30.00027292126
95-th percentile0.00032030748
Maximum1
Range1
Interquartile range (IQR)0.0002613902

Descriptive statistics

Standard deviation0.004678797
Coefficient of variation (CV)17.146799
Kurtosis15377.16
Mean0.00027286707
Median Absolute Deviation (MAD)4.6525304 × 10-5
Skewness100.95908
Sum54.273261
Variance2.1891141 × 10-5
MonotonicityNot monotonic
2024-08-30T20:20:40.590707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0002729212582 55741
28.0%
1.2812287 × 10-55663
 
2.8%
6.406137092 × 10-65645
 
2.8%
2.56245868 × 10-54902
 
2.5%
1.92184369 × 10-54144
 
2.1%
3.843688661 × 10-53413
 
1.7%
3.203073671 × 10-53259
 
1.6%
3.84367713 × 10-63144
 
1.6%
2.562447149 × 10-63031
 
1.5%
6.406148623 × 10-53018
 
1.5%
Other values (12859) 106940
53.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1.524663677 × 10-91
 
< 0.1%
2.549647662 × 10-94
 
< 0.1%
6.393337604 × 10-94
 
< 0.1%
1.279948751 × 10-814
< 0.1%
1.536194747 × 10-81
 
< 0.1%
1.664317745 × 10-81
 
< 0.1%
1.792440743 × 10-86
 
< 0.1%
2.561178732 × 10-817
< 0.1%
3.201793722 × 10-86
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.66623959 1
 
< 0.1%
0.5124919923 2
< 0.1%
0.4307495195 1
 
< 0.1%
0.3459320948 1
 
< 0.1%
0.3408859526 1
 
< 0.1%
0.3062139654 1
 
< 0.1%
0.269058296 1
 
< 0.1%
0.2660858424 1
 
< 0.1%
0.2562459961 4
< 0.1%
Distinct94
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.8 MiB
2024-08-30T20:20:41.431064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length12.781508
Min length4

Characters and Unicode

Total characters2542242
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowDesconocido
2nd row1 family dwelling
3rd row1 family dwelling
4th row2 family dwelling
5th rowapartments
ValueCountFrequency (%)
family 67753
19.0%
dwelling 67753
19.0%
1 46766
13.1%
desconocido 41114
11.5%
apartments 40798
11.4%
office 24616
 
6.9%
2 20987
 
5.9%
sales 7032
 
2.0%
retail 6910
 
1.9%
food/beverage 4886
 
1.4%
Other values (140) 28914
8.1%
2024-08-30T20:20:42.780872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 235958
 
9.3%
i 221886
 
8.7%
e 220229
 
8.7%
a 187975
 
7.4%
o 173288
 
6.8%
n 171228
 
6.7%
158629
 
6.2%
f 123921
 
4.9%
d 122543
 
4.8%
c 114773
 
4.5%
Other values (29) 811812
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2542242
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 235958
 
9.3%
i 221886
 
8.7%
e 220229
 
8.7%
a 187975
 
7.4%
o 173288
 
6.8%
n 171228
 
6.7%
158629
 
6.2%
f 123921
 
4.9%
d 122543
 
4.8%
c 114773
 
4.5%
Other values (29) 811812
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2542242
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 235958
 
9.3%
i 221886
 
8.7%
e 220229
 
8.7%
a 187975
 
7.4%
o 173288
 
6.8%
n 171228
 
6.7%
158629
 
6.2%
f 123921
 
4.9%
d 122543
 
4.8%
c 114773
 
4.5%
Other values (29) 811812
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2542242
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 235958
 
9.3%
i 221886
 
8.7%
e 220229
 
8.7%
a 187975
 
7.4%
o 173288
 
6.8%
n 171228
 
6.7%
158629
 
6.2%
f 123921
 
4.9%
d 122543
 
4.8%
c 114773
 
4.5%
Other values (29) 811812
31.9%

Existing Units
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct348
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0062223069
Minimum0
Maximum1
Zeros29134
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-30T20:20:43.165931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00052438385
median0.00052438385
Q30.0010487677
95-th percentile0.022024122
Maximum1
Range1
Interquartile range (IQR)0.00052438385

Descriptive statistics

Standard deviation0.033784195
Coefficient of variation (CV)5.4295289
Kurtosis319.74268
Mean0.0062223069
Median Absolute Deviation (MAD)0.00052438385
Skewness14.90048
Sum1237.6168
Variance0.0011413718
MonotonicityNot monotonic
2024-08-30T20:20:43.669581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.000524383849 98884
49.7%
0 29134
 
14.6%
0.001048767698 21804
 
11.0%
0.001573151547 8616
 
4.3%
0.003146303094 6066
 
3.0%
0.002097535396 5364
 
2.7%
0.006292606188 2535
 
1.3%
0.002621919245 2347
 
1.2%
0.004195070792 1728
 
0.9%
0.004719454641 1188
 
0.6%
Other values (338) 21234
 
10.7%
ValueCountFrequency (%)
0 29134
 
14.6%
0.0001573151547 1
 
< 0.1%
0.000524383849 98884
49.7%
0.001048767698 21804
 
11.0%
0.001573151547 8616
 
4.3%
0.002097535396 5364
 
2.7%
0.002621919245 2347
 
1.2%
0.003146303094 6066
 
3.0%
0.003670686943 1172
 
0.6%
0.004195070792 1728
 
0.9%
ValueCountFrequency (%)
1 44
< 0.1%
0.9082328264 8
 
< 0.1%
0.7865757735 51
< 0.1%
0.7860513896 1
 
< 0.1%
0.6219192449 51
< 0.1%
0.5296276875 2
 
< 0.1%
0.5270057682 21
< 0.1%
0.5264813844 2
 
< 0.1%
0.524383849 1
 
< 0.1%
0.4971158888 1
 
< 0.1%
Distinct104
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 MiB
2024-08-30T20:20:44.799736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length30
Median length23
Mean length12.796802
Min length4

Characters and Unicode

Total characters2545284
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowDesconocido
2nd row1 family dwelling
3rd row1 family dwelling
4th row2 family dwelling
5th rowapartments
ValueCountFrequency (%)
family 68632
19.1%
dwelling 68632
19.1%
1 46529
13.0%
apartments 43279
12.1%
desconocido 38804
10.8%
office 24523
 
6.8%
2 22103
 
6.2%
sales 6264
 
1.7%
retail 6126
 
1.7%
food/beverage 5660
 
1.6%
Other values (152) 28140
7.8%
2024-08-30T20:20:46.208226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 236566
 
9.3%
e 221201
 
8.7%
i 220114
 
8.6%
a 185143
 
7.3%
n 172023
 
6.8%
o 166773
 
6.6%
159792
 
6.3%
f 125224
 
4.9%
d 123162
 
4.8%
m 117066
 
4.6%
Other values (30) 818220
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2545284
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 236566
 
9.3%
e 221201
 
8.7%
i 220114
 
8.6%
a 185143
 
7.3%
n 172023
 
6.8%
o 166773
 
6.6%
159792
 
6.3%
f 125224
 
4.9%
d 123162
 
4.8%
m 117066
 
4.6%
Other values (30) 818220
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2545284
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 236566
 
9.3%
e 221201
 
8.7%
i 220114
 
8.6%
a 185143
 
7.3%
n 172023
 
6.8%
o 166773
 
6.6%
159792
 
6.3%
f 125224
 
4.9%
d 123162
 
4.8%
m 117066
 
4.6%
Other values (30) 818220
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2545284
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 236566
 
9.3%
e 221201
 
8.7%
i 220114
 
8.6%
a 185143
 
7.3%
n 172023
 
6.8%
o 166773
 
6.6%
159792
 
6.3%
f 125224
 
4.9%
d 123162
 
4.8%
m 117066
 
4.6%
Other values (30) 818220
32.1%

Proposed Units
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct370
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0067083717
Minimum0
Maximum1
Zeros28632
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-30T20:20:46.602424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00052328624
median0.00052328624
Q30.0015698587
95-th percentile0.025117739
Maximum1
Range1
Interquartile range (IQR)0.0010465725

Descriptive statistics

Standard deviation0.034679413
Coefficient of variation (CV)5.1695724
Kurtosis286.96287
Mean0.0067083717
Median Absolute Deviation (MAD)0.00052328624
Skewness13.987626
Sum1334.2951
Variance0.0012026617
MonotonicityNot monotonic
2024-08-30T20:20:47.083893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0005232862376 95596
48.1%
0 28632
 
14.4%
0.001046572475 22943
 
11.5%
0.001569858713 9371
 
4.7%
0.003139717425 5903
 
3.0%
0.00209314495 5530
 
2.8%
0.006279434851 2477
 
1.2%
0.002616431188 2402
 
1.2%
0.004186289901 1811
 
0.9%
0.003663003663 1339
 
0.7%
Other values (360) 22896
 
11.5%
ValueCountFrequency (%)
0 28632
 
14.4%
0.0005232862376 95596
48.1%
0.001046572475 22943
 
11.5%
0.001569858713 9371
 
4.7%
0.00209314495 5530
 
2.8%
0.002616431188 2402
 
1.2%
0.003139717425 5903
 
3.0%
0.003663003663 1339
 
0.7%
0.004186289901 1811
 
0.9%
0.004709576138 1275
 
0.6%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.997906855 43
< 0.1%
0.9063317635 8
 
< 0.1%
0.7849293564 51
< 0.1%
0.7844060701 1
 
< 0.1%
0.6274201988 1
 
< 0.1%
0.6206174778 50
< 0.1%
0.5306122449 4
 
< 0.1%
0.5285190999 2
 
< 0.1%
0.5259026688 19
 
< 0.1%

Plansets
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing37309
Missing (%)18.8%
Infinite0
Infinite (%)0.0%
Mean1.2746502
Minimum0
Maximum9000
Zeros63244
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-30T20:20:47.442539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q32
95-th percentile2
Maximum9000
Range9000
Interquartile range (IQR)2

Descriptive statistics

Standard deviation22.407345
Coefficient of variation (CV)17.579211
Kurtosis160974.06
Mean1.2746502
Median Absolute Deviation (MAD)0
Skewness400.83221
Sum205972
Variance502.08913
MonotonicityNot monotonic
2024-08-30T20:20:47.774497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 98088
49.3%
0 63244
31.8%
3 250
 
0.1%
4 3
 
< 0.1%
6 2
 
< 0.1%
1 2
 
< 0.1%
20 1
 
< 0.1%
9000 1
 
< 0.1%
(Missing) 37309
 
18.8%
ValueCountFrequency (%)
0 63244
31.8%
1 2
 
< 0.1%
2 98088
49.3%
3 250
 
0.1%
4 3
 
< 0.1%
6 2
 
< 0.1%
20 1
 
< 0.1%
9000 1
 
< 0.1%
ValueCountFrequency (%)
9000 1
 
< 0.1%
20 1
 
< 0.1%
6 2
 
< 0.1%
4 3
 
< 0.1%
3 250
 
0.1%
2 98088
49.3%
1 2
 
< 0.1%
0 63244
31.8%

TIDF Compliance
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
198898 
True
 
2
ValueCountFrequency (%)
False 198898
> 99.9%
True 2
 
< 0.1%
2024-08-30T20:20:48.144045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Existing Construction Type
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4695073
Minimum-99999
Maximum99999
Zeros0
Zeros (%)0.0%
Negative43367
Negative (%)21.8%
Memory size3.0 MiB
2024-08-30T20:20:48.444598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-99999
5-th percentile-1
Q11
median5
Q35
95-th percentile5
Maximum99999
Range199998
Interquartile range (IQR)4

Descriptive statistics

Standard deviation501.38005
Coefficient of variation (CV)144.51045
Kurtosis39775.909
Mean3.4695073
Median Absolute Deviation (MAD)0
Skewness39.868647
Sum690085
Variance251381.96
MonotonicityNot monotonic
2024-08-30T20:20:48.884848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 113346
57.0%
-1 43365
 
21.8%
1 28072
 
14.1%
3 9663
 
4.9%
2 4068
 
2.0%
4 381
 
0.2%
99999 3
 
< 0.1%
-99999 2
 
< 0.1%
ValueCountFrequency (%)
-99999 2
 
< 0.1%
-1 43365
 
21.8%
1 28072
 
14.1%
2 4068
 
2.0%
3 9663
 
4.9%
4 381
 
0.2%
5 113346
57.0%
99999 3
 
< 0.1%
ValueCountFrequency (%)
99999 3
 
< 0.1%
5 113346
57.0%
4 381
 
0.2%
3 9663
 
4.9%
2 4068
 
2.0%
1 28072
 
14.1%
-1 43365
 
21.8%
-99999 2
 
< 0.1%

Existing Construction Type Description
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.9 MiB
wood frame (5)
113350 
NON AVAILABLE
43366 
constr type 1
28072 
constr type 3
 
9663
constr type 2
 
4068

Length

Max length14
Median length14
Mean length13.569884
Min length13

Characters and Unicode

Total characters2699050
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNON AVAILABLE
2nd rowwood frame (5)
3rd rowwood frame (5)
4th rowwood frame (5)
5th rowwood frame (5)

Common Values

ValueCountFrequency (%)
wood frame (5) 113350
57.0%
NON AVAILABLE 43366
 
21.8%
constr type 1 28072
 
14.1%
constr type 3 9663
 
4.9%
constr type 2 4068
 
2.0%
constr type 4 381
 
0.2%

Length

2024-08-30T20:20:49.285655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-30T20:20:49.663030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
wood 113350
20.5%
frame 113350
20.5%
5 113350
20.5%
non 43366
 
7.8%
available 43366
 
7.8%
constr 42184
 
7.6%
type 42184
 
7.6%
1 28072
 
5.1%
3 9663
 
1.7%
2 4068
 
0.7%

Most occurring characters

ValueCountFrequency (%)
354434
 
13.1%
o 268884
 
10.0%
e 155534
 
5.8%
r 155534
 
5.8%
A 130098
 
4.8%
w 113350
 
4.2%
) 113350
 
4.2%
5 113350
 
4.2%
( 113350
 
4.2%
m 113350
 
4.2%
Other values (20) 1067816
39.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2699050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
354434
 
13.1%
o 268884
 
10.0%
e 155534
 
5.8%
r 155534
 
5.8%
A 130098
 
4.8%
w 113350
 
4.2%
) 113350
 
4.2%
5 113350
 
4.2%
( 113350
 
4.2%
m 113350
 
4.2%
Other values (20) 1067816
39.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2699050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
354434
 
13.1%
o 268884
 
10.0%
e 155534
 
5.8%
r 155534
 
5.8%
A 130098
 
4.8%
w 113350
 
4.2%
) 113350
 
4.2%
5 113350
 
4.2%
( 113350
 
4.2%
m 113350
 
4.2%
Other values (20) 1067816
39.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2699050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
354434
 
13.1%
o 268884
 
10.0%
e 155534
 
5.8%
r 155534
 
5.8%
A 130098
 
4.8%
w 113350
 
4.2%
) 113350
 
4.2%
5 113350
 
4.2%
( 113350
 
4.2%
m 113350
 
4.2%
Other values (20) 1067816
39.6%

Proposed Construction Type
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.985083
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative43162
Negative (%)21.7%
Memory size3.0 MiB
2024-08-30T20:20:50.042197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11
median5
Q35
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5205052
Coefficient of variation (CV)0.8443669
Kurtosis-1.3547659
Mean2.985083
Median Absolute Deviation (MAD)0
Skewness-0.63327424
Sum593733
Variance6.3529467
MonotonicityNot monotonic
2024-08-30T20:20:50.332037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 114382
57.5%
-1 43162
 
21.7%
1 27841
 
14.0%
3 9360
 
4.7%
2 3778
 
1.9%
4 377
 
0.2%
ValueCountFrequency (%)
-1 43162
 
21.7%
1 27841
 
14.0%
2 3778
 
1.9%
3 9360
 
4.7%
4 377
 
0.2%
5 114382
57.5%
ValueCountFrequency (%)
5 114382
57.5%
4 377
 
0.2%
3 9360
 
4.7%
2 3778
 
1.9%
1 27841
 
14.0%
-1 43162
 
21.7%

Proposed Construction Type Description
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.9 MiB
wood frame (5)
114382 
NON AVAILABLE
43162 
constr type 1
27841 
constr type 3
 
9360
constr type 2
 
3778

Length

Max length14
Median length14
Mean length13.575073
Min length13

Characters and Unicode

Total characters2700082
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNON AVAILABLE
2nd rowwood frame (5)
3rd rowwood frame (5)
4th rowwood frame (5)
5th rowwood frame (5)

Common Values

ValueCountFrequency (%)
wood frame (5) 114382
57.5%
NON AVAILABLE 43162
 
21.7%
constr type 1 27841
 
14.0%
constr type 3 9360
 
4.7%
constr type 2 3778
 
1.9%
constr type 4 377
 
0.2%

Length

2024-08-30T20:20:50.748394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-30T20:20:51.133357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
wood 114382
20.7%
frame 114382
20.7%
5 114382
20.7%
non 43162
 
7.8%
available 43162
 
7.8%
constr 41356
 
7.5%
type 41356
 
7.5%
1 27841
 
5.0%
3 9360
 
1.7%
2 3778
 
0.7%

Most occurring characters

ValueCountFrequency (%)
354638
 
13.1%
o 270120
 
10.0%
e 155738
 
5.8%
r 155738
 
5.8%
A 129486
 
4.8%
w 114382
 
4.2%
) 114382
 
4.2%
5 114382
 
4.2%
( 114382
 
4.2%
m 114382
 
4.2%
Other values (20) 1062452
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2700082
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
354638
 
13.1%
o 270120
 
10.0%
e 155738
 
5.8%
r 155738
 
5.8%
A 129486
 
4.8%
w 114382
 
4.2%
) 114382
 
4.2%
5 114382
 
4.2%
( 114382
 
4.2%
m 114382
 
4.2%
Other values (20) 1062452
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2700082
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
354638
 
13.1%
o 270120
 
10.0%
e 155738
 
5.8%
r 155738
 
5.8%
A 129486
 
4.8%
w 114382
 
4.2%
) 114382
 
4.2%
5 114382
 
4.2%
( 114382
 
4.2%
m 114382
 
4.2%
Other values (20) 1062452
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2700082
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
354638
 
13.1%
o 270120
 
10.0%
e 155738
 
5.8%
r 155738
 
5.8%
A 129486
 
4.8%
w 114382
 
4.2%
) 114382
 
4.2%
5 114382
 
4.2%
( 114382
 
4.2%
m 114382
 
4.2%
Other values (20) 1062452
39.3%

Site Permit
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
False
193541 
True
 
5359
ValueCountFrequency (%)
False 193541
97.3%
True 5359
 
2.7%
2024-08-30T20:20:51.516651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Supervisor District
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4820312
Minimum-1
Maximum20
Zeros0
Zeros (%)0.0%
Negative1719
Negative (%)0.9%
Memory size3.0 MiB
2024-08-30T20:20:51.834381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum20
Range21
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.937832
Coefficient of variation (CV)0.53590209
Kurtosis-1.0840767
Mean5.4820312
Median Absolute Deviation (MAD)3
Skewness0.058411973
Sum1090376
Variance8.6308568
MonotonicityNot monotonic
2024-08-30T20:20:52.117017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 28648
14.4%
8 26760
13.5%
2 25483
12.8%
6 24795
12.5%
5 19044
9.6%
9 16361
8.2%
7 14365
7.2%
1 13038
6.6%
10 12153
6.1%
4 9592
 
4.8%
Other values (4) 8661
 
4.4%
ValueCountFrequency (%)
-1 1719
 
0.9%
1 13038
6.6%
2 25483
12.8%
3 28648
14.4%
4 9592
 
4.8%
5 19044
9.6%
6 24795
12.5%
7 14365
7.2%
8 26760
13.5%
9 16361
8.2%
ValueCountFrequency (%)
20 1
 
< 0.1%
15 1
 
< 0.1%
11 6940
 
3.5%
10 12153
6.1%
9 16361
8.2%
8 26760
13.5%
7 14365
7.2%
6 24795
12.5%
5 19044
9.6%
4 9592
 
4.8%

Neighborhoods - Analysis Boundaries
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.1 MiB
Financial District/South Beach
21816 
Mission
14681 
Sunset/Parkside
 
10207
West of Twin Peaks
 
8739
Castro/Upper Market
 
8527
Other values (37)
134930 

Length

Max length30
Median length18
Mean length14.765591
Min length6

Characters and Unicode

Total characters2936876
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNob Hill
2nd rowSunset/Parkside
3rd rowBernal Heights
4th rowPacific Heights
5th rowMission

Common Values

ValueCountFrequency (%)
Financial District/South Beach 21816
 
11.0%
Mission 14681
 
7.4%
Sunset/Parkside 10207
 
5.1%
West of Twin Peaks 8739
 
4.4%
Castro/Upper Market 8527
 
4.3%
Pacific Heights 8508
 
4.3%
Marina 8244
 
4.1%
Outer Richmond 7854
 
3.9%
Noe Valley 7844
 
3.9%
South of Market 7572
 
3.8%
Other values (32) 94908
47.7%

Length

2024-08-30T20:20:52.442182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
beach 25870
 
6.5%
financial 21816
 
5.5%
district/south 21816
 
5.5%
mission 19210
 
4.8%
heights 18659
 
4.7%
of 16311
 
4.1%
market 16099
 
4.0%
hill 15797
 
4.0%
valley 14233
 
3.6%
richmond 12312
 
3.1%
Other values (48) 216386
54.3%

Most occurring characters

ValueCountFrequency (%)
i 281022
 
9.6%
e 234489
 
8.0%
a 203688
 
6.9%
199609
 
6.8%
n 198628
 
6.8%
t 194560
 
6.6%
s 176836
 
6.0%
o 150135
 
5.1%
r 148180
 
5.0%
c 109197
 
3.7%
Other values (36) 1040532
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2936876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 281022
 
9.6%
e 234489
 
8.0%
a 203688
 
6.9%
199609
 
6.8%
n 198628
 
6.8%
t 194560
 
6.6%
s 176836
 
6.0%
o 150135
 
5.1%
r 148180
 
5.0%
c 109197
 
3.7%
Other values (36) 1040532
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2936876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 281022
 
9.6%
e 234489
 
8.0%
a 203688
 
6.9%
199609
 
6.8%
n 198628
 
6.8%
t 194560
 
6.6%
s 176836
 
6.0%
o 150135
 
5.1%
r 148180
 
5.0%
c 109197
 
3.7%
Other values (36) 1040532
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2936876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 281022
 
9.6%
e 234489
 
8.0%
a 203688
 
6.9%
199609
 
6.8%
n 198628
 
6.8%
t 194560
 
6.6%
s 176836
 
6.0%
o 150135
 
5.1%
r 148180
 
5.0%
c 109197
 
3.7%
Other values (36) 1040532
35.4%

Zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93303.515
Minimum-1
Maximum94158
Zeros0
Zeros (%)0.0%
Negative1716
Negative (%)0.9%
Memory size3.0 MiB
2024-08-30T20:20:52.947143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile94103
Q194109
median94114
Q394122
95-th percentile94133
Maximum94158
Range94159
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8704.1587
Coefficient of variation (CV)0.093288647
Kurtosis110.92036
Mean93303.515
Median Absolute Deviation (MAD)7
Skewness-10.62634
Sum1.8558069 × 1010
Variance75762379
MonotonicityNot monotonic
2024-08-30T20:20:53.364678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
94110 17837
 
9.0%
94114 13404
 
6.7%
94117 11780
 
5.9%
94109 11348
 
5.7%
94103 10986
 
5.5%
94115 10095
 
5.1%
94118 9812
 
4.9%
94123 9515
 
4.8%
94122 8886
 
4.5%
94105 8628
 
4.3%
Other values (18) 86609
43.5%
ValueCountFrequency (%)
-1 1716
 
0.9%
94102 7164
3.6%
94103 10986
5.5%
94104 4229
 
2.1%
94105 8628
4.3%
94107 7706
3.9%
94108 5320
 
2.7%
94109 11348
5.7%
94110 17837
9.0%
94111 5385
 
2.7%
ValueCountFrequency (%)
94158 1058
 
0.5%
94134 2983
 
1.5%
94133 7424
3.7%
94132 3507
 
1.8%
94131 7664
3.9%
94130 81
 
< 0.1%
94129 23
 
< 0.1%
94127 4993
2.5%
94124 5265
2.6%
94123 9515
4.8%
Distinct57604
Distinct (%)29.2%
Missing1700
Missing (%)0.9%
Memory size19.8 MiB
2024-08-30T20:20:54.248625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length41
Median length40
Mean length40.04427
Min length35

Characters and Unicode

Total characters7896730
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26166 ?
Unique (%)13.3%

Sample

1st row(37.79362102799777, -122.41488237355445)
2nd row(37.759041020475465, -122.50286985467523)
3rd row(37.73778863007536, -122.41197863877355)
4th row(37.78762264983362, -122.43099126735969)
5th row(37.75275550565926, -122.41707462095194)
ValueCountFrequency (%)
37.79226164705184 554
 
0.1%
122.4034859571375 554
 
0.1%
37.79294896659241 330
 
0.1%
122.39809861435491 330
 
0.1%
37.728556952954136 281
 
0.1%
122.47676641508518 281
 
0.1%
37.77523036414975 276
 
0.1%
122.4174703200545 276
 
0.1%
37.78977799888473 252
 
0.1%
122.40173648131338 252
 
0.1%
Other values (115177) 391014
99.1%
2024-08-30T20:20:55.360547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 899219
11.4%
7 893432
11.3%
3 736552
9.3%
1 703883
8.9%
4 698280
8.8%
5 534294
 
6.8%
6 524215
 
6.6%
8 524170
 
6.6%
9 523462
 
6.6%
0 478823
 
6.1%
Other values (6) 1380400
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7896730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 899219
11.4%
7 893432
11.3%
3 736552
9.3%
1 703883
8.9%
4 698280
8.8%
5 534294
 
6.8%
6 524215
 
6.6%
8 524170
 
6.6%
9 523462
 
6.6%
0 478823
 
6.1%
Other values (6) 1380400
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7896730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 899219
11.4%
7 893432
11.3%
3 736552
9.3%
1 703883
8.9%
4 698280
8.8%
5 534294
 
6.8%
6 524215
 
6.6%
8 524170
 
6.6%
9 523462
 
6.6%
0 478823
 
6.1%
Other values (6) 1380400
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7896730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 899219
11.4%
7 893432
11.3%
3 736552
9.3%
1 703883
8.9%
4 698280
8.8%
5 534294
 
6.8%
6 524215
 
6.6%
8 524170
 
6.6%
9 523462
 
6.6%
0 478823
 
6.1%
Other values (6) 1380400
17.5%

Record ID
Real number (ℝ)

UNIQUE 

Distinct198900
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1620476 × 1012
Minimum1.2935322 × 1010
Maximum1.4983421 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-30T20:20:55.707949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.2935322 × 1010
5-th percentile1.3453444 × 1011
Q11.3085667 × 1012
median1.3718398 × 1012
Q31.4349996 × 1012
95-th percentile1.4859054 × 1012
Maximum1.4983421 × 1012
Range1.4854068 × 1012
Interquartile range (IQR)1.2643288 × 1011

Descriptive statistics

Standard deviation4.9182154 × 1011
Coefficient of variation (CV)0.42323701
Kurtosis0.55167729
Mean1.1620476 × 1012
Median Absolute Deviation (MAD)6.3217604 × 1010
Skewness-1.5726912
Sum2.3113126 × 1017
Variance2.4188842 × 1023
MonotonicityNot monotonic
2024-08-30T20:20:56.162104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.464153233 × 10121
 
< 0.1%
1.474831103 × 10121
 
< 0.1%
1.472149495 × 10121
 
< 0.1%
1.462796863 × 10111
 
< 0.1%
1.40211042 × 10121
 
< 0.1%
1.319225265 × 10121
 
< 0.1%
1.450474223 × 10121
 
< 0.1%
1.490112504 × 10121
 
< 0.1%
1.328877132 × 10121
 
< 0.1%
1.379653138 × 10121
 
< 0.1%
Other values (198890) 198890
> 99.9%
ValueCountFrequency (%)
1.29353215 × 10101
< 0.1%
1.295722223 × 10101
< 0.1%
1.296226196 × 10101
< 0.1%
1.296342101 × 10101
< 0.1%
1.296629102 × 10101
< 0.1%
1.297286249 × 10101
< 0.1%
1.297668221 × 10101
< 0.1%
1.297846157 × 10101
< 0.1%
1.298230162 × 10101
< 0.1%
1.298784182 × 10101
< 0.1%
ValueCountFrequency (%)
1.498342128 × 10121
< 0.1%
1.498341156 × 10121
< 0.1%
1.498339194 × 10121
< 0.1%
1.498338347 × 10121
< 0.1%
1.498337117 × 10121
< 0.1%
1.498336196 × 10121
< 0.1%
1.498335409 × 10121
< 0.1%
1.498334222 × 10121
< 0.1%
1.498331399 × 10121
< 0.1%
1.498330476 × 10121
< 0.1%

Interactions

2024-08-30T20:19:59.720037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:18:26.981899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:18:33.014966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:18:38.632781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:18:45.454062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:18:51.018288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:18:57.125862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:19:03.338316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:19:09.349632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-08-30T20:20:05.554115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:18:32.588438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:18:38.244458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:18:45.053434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:18:50.635783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:18:56.726259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:19:02.937776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:19:08.949080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:19:14.798826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:19:20.780768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:19:26.362611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:19:33.176150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:19:44.866827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:19:52.210586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T20:19:59.265077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-08-30T20:20:56.578910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Current StatusEstimated CostExisting Construction TypeExisting Construction Type DescriptionExisting UnitsFire Only PermitNeighborhoods - Analysis BoundariesNumber of Existing StoriesNumber of Proposed StoriesPermit TypePermit Type DefinitionPlansetsProposed Construction TypeProposed Construction Type DescriptionProposed UnitsRecord IDRevised CostSite PermitStreet NumberStreet SuffixStructural NotificationSupervisor DistrictTIDF ComplianceUnitVoluntary Soft-Story RetrofitZipcode
Current Status1.0000.0150.2980.2330.0100.1300.0400.0430.0420.1350.1350.0000.2360.2360.0090.1060.0130.2130.0180.0260.1350.0260.0000.0000.0030.021
Estimated Cost0.0151.000-0.4650.015-0.1280.0010.009-0.295-0.256-0.0740.1560.242-0.4200.019-0.1130.0120.9040.1170.0170.0000.011-0.0510.000-0.0030.000-0.049
Existing Construction Type0.298-0.4651.0000.5770.3720.1220.1020.3260.254-0.0480.130-0.3510.9410.5150.3390.044-0.4160.0050.0750.0310.0980.0960.000-0.0220.0060.245
Existing Construction Type Description0.2330.0150.5771.0000.1150.3940.3610.3740.3680.1220.1220.0000.9420.9420.1070.0790.0120.0760.1000.1220.1550.1910.0000.0510.0100.052
Existing Units0.010-0.1280.3720.1151.0000.0660.1100.2200.1910.0040.007-0.1980.3720.1130.9650.047-0.1100.0160.1430.0560.021-0.0660.0000.2410.0000.083
Fire Only Permit0.1300.0010.1220.3940.0661.0000.3550.3470.3480.0550.0550.0000.4170.4170.0690.0390.0000.0540.1060.1070.0610.1830.0000.0080.0030.008
Neighborhoods - Analysis Boundaries0.0400.0090.1020.3610.1100.3551.0000.2490.2510.0930.0930.0000.3550.3550.1110.3670.0070.1060.3200.2100.1250.7620.0000.0550.0110.997
Number of Existing Stories0.043-0.2950.3260.3740.2200.3470.2491.0000.947-0.0320.0330.2360.2800.3630.197-0.069-0.2560.057-0.1210.0830.068-0.1500.0000.1420.000-0.263
Number of Proposed Stories0.042-0.2560.2540.3680.1910.3480.2510.9471.000-0.1040.0380.2920.2740.3690.225-0.063-0.2160.046-0.1360.0830.069-0.1330.0000.1290.000-0.269
Permit Type0.135-0.074-0.0480.1220.0040.0550.093-0.032-0.1041.0001.000-0.285-0.0290.144-0.042-0.013-0.0830.614-0.0050.0580.396-0.0490.0100.0470.023-0.002
Permit Type Definition0.1350.1560.1300.1220.0070.0550.0930.0330.0381.0001.0000.0000.1440.1440.0180.0230.1340.6140.0170.0580.3960.0430.0100.0040.0230.021
Plansets0.0000.242-0.3510.000-0.1980.0000.0000.2360.292-0.2850.0001.000-0.3420.000-0.165-0.0150.2850.000-0.0640.0000.000-0.0520.000-0.0400.000-0.200
Proposed Construction Type0.236-0.4200.9410.9420.3720.4170.3550.2800.274-0.0290.144-0.3421.0001.0000.3730.052-0.3700.1110.0590.1240.1550.1140.000-0.0270.0100.264
Proposed Construction Type Description0.2360.0190.5150.9420.1130.4170.3550.3630.3690.1440.1440.0001.0001.0000.1160.0800.0170.1110.1000.1240.1550.1900.0000.0510.0100.054
Proposed Units0.009-0.1130.3390.1070.9650.0690.1110.1970.225-0.0420.018-0.1650.3730.1161.0000.052-0.0940.0030.1360.0560.021-0.0530.0000.2300.0000.075
Record ID0.1060.0120.0440.0790.0470.0390.367-0.069-0.063-0.0130.023-0.0150.0520.0800.0521.0000.0140.013-0.0230.0980.0070.2730.0000.0860.005-0.005
Revised Cost0.0130.904-0.4160.012-0.1100.0000.007-0.256-0.216-0.0830.1340.285-0.3700.017-0.0940.0141.0000.1000.0220.0000.005-0.0580.0000.0000.000-0.050
Site Permit0.2130.1170.0050.0760.0160.0540.1060.0570.0460.6140.6140.0000.1110.1110.0030.0130.1001.0000.0130.1230.5060.0740.0000.0100.0000.015
Street Number0.0180.0170.0750.1000.1430.1060.320-0.121-0.136-0.0050.017-0.0640.0590.1000.136-0.0230.0220.0131.0000.1190.020-0.1730.0000.0460.0000.147
Street Suffix0.0260.0000.0310.1220.0560.1070.2100.0830.0830.0580.0580.0000.1240.1240.0560.0980.0000.1230.1191.0000.0570.1830.0000.0290.0180.037
Structural Notification0.1350.0110.0980.1550.0210.0610.1250.0680.0690.3960.3960.0000.1550.1550.0210.0070.0050.5060.0200.0571.0000.0670.0000.0110.0000.017
Supervisor District0.026-0.0510.0960.191-0.0660.1830.762-0.150-0.133-0.0490.043-0.0520.1140.190-0.0530.273-0.0580.074-0.1730.1830.0671.0000.000-0.1220.006-0.101
TIDF Compliance0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
Unit0.000-0.003-0.0220.0510.2410.0080.0550.1420.1290.0470.004-0.040-0.0270.0510.2300.0860.0000.0100.0460.0290.011-0.1220.0001.0000.000-0.070
Voluntary Soft-Story Retrofit0.0030.0000.0060.0100.0000.0030.0110.0000.0000.0230.0230.0000.0100.0100.0000.0050.0000.0000.0000.0180.0000.0060.0000.0001.0000.000
Zipcode0.021-0.0490.2450.0520.0830.0080.997-0.263-0.269-0.0020.021-0.2000.2640.0540.075-0.005-0.0500.0150.1470.0370.017-0.1010.000-0.0700.0001.000

Missing values

2024-08-30T20:20:06.370796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-30T20:20:08.906267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-30T20:20:11.541728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Permit NumberPermit TypePermit Type DefinitionPermit Creation DateBlockLotStreet NumberStreet NameStreet SuffixUnitDescriptionCurrent StatusCurrent Status DateFiled DateIssued DateCompleted DateFirst Construction Document DateStructural NotificationNumber of Existing StoriesNumber of Proposed StoriesVoluntary Soft-Story RetrofitFire Only PermitPermit Expiration DateEstimated CostRevised CostExisting UseExisting UnitsProposed UseProposed UnitsPlansetsTIDF ComplianceExisting Construction TypeExisting Construction Type DescriptionProposed Construction TypeProposed Construction Type DescriptionSite PermitSupervisor DistrictNeighborhoods - Analysis BoundariesZipcodeLocationRecord ID
0M7889278otc alterations permit23/05/201702150010.16JonesSt0.00street spaceissued23/05/201723/05/201723/05/201701/01/190023/05/2017False0.000.00FalseFalse21/02/20240.000.00Desconocido0.00Desconocido0.00NaNFalse-1.00NON AVAILABLE-1.00NON AVAILABLEFalse3.00Nob Hill94109.00(37.79362102799777, -122.41488237355445)1464153232862
12013053183568otc alterations permit31/05/20131810017A0.1843RdAv0.00remodel kitchen: replace countertop, cabinets, sink, stove, hardwood floor & lighting.complete28/08/201331/05/201303/06/2013NaN03/06/2013False0.040.04FalseFalse29/05/20140.000.001 family dwelling0.001 family dwelling0.002.00False5.00wood frame (5)5.00wood frame (5)False4.00Sunset/Parkside94122.00(37.759041020475465, -122.50286985467523)1306559115258
22017051062058otc alterations permit10/05/201757000270.05PrentissSt0.00replacement of 4 windows; 2 located in lightwell and 2 at front. replaceing existing vinyl windows with new wood-clad windows. no horizontal mullion in living room windowissued11/05/201710/05/201711/05/201701/01/190011/05/2017False0.040.04FalseFalse06/05/20180.000.001 family dwelling0.001 family dwelling0.000.00False5.00wood frame (5)5.00wood frame (5)False9.00Bernal Heights94110.00(37.73778863007536, -122.41197863877355)1462579187173
32014102799838otc alterations permit27/10/201406610050.24BushSt0.00two kitchens & two bathrooms remodel. replace kitchen cabinets, countertops & appliances in same location. replace tub & faucet & vanity & tile in same location in both units.complete31/12/201427/10/201427/10/2014NaN27/10/2014False0.040.04FalseFalse22/10/20150.000.002 family dwelling0.002 family dwelling0.000.00False5.00wood frame (5)5.00wood frame (5)False5.00Pacific Heights94115.00(37.78762264983362, -122.43099126735969)136037778128
42013102803888otc alterations permit28/10/20133642051A0.10CappSt0.00reroofingissued28/10/201328/10/201328/10/201301/01/190028/10/2013False0.040.04FalseFalse23/10/20140.000.00apartments0.00apartments0.000.00False5.00wood frame (5)5.00wood frame (5)False9.00Mission94110.00(37.75275550565926, -122.41707462095194)1322242163712
52013071619258otc alterations permit16/07/201335650760.4116ThSt0.00to comply with physical inspection report #cc-7260 item #1 -all storage items are being removed.complete08/10/201316/07/201316/07/201310/08/201316/07/2013False0.050.05FalseFalse11/07/20140.000.00apartments0.00apartments0.000.00False5.00wood frame (5)5.00wood frame (5)False8.00Castro/Upper Market94114.00(37.76416871595274, -122.43039745629406)1311167285194
6M5016078otc alterations permit02/07/201435610070.03NoeSt0.00street spaceissued02/07/201402/07/201402/07/201401/01/190002/07/2014False0.000.00FalseFalse21/02/20240.000.00Desconocido0.00Desconocido0.00NaNFalse-1.00NON AVAILABLE-1.00NON AVAILABLEFalse8.00Castro/Upper Market94114.00(37.76519640087225, -122.4334386869886)1347642247334
7M7878878otc alterations permit19/05/201706540090.28PineSt0.00street space permitissued19/05/201719/05/201719/05/201701/01/190019/05/2017False0.000.00FalseFalse21/02/20240.000.00Desconocido0.00Desconocido0.00NaNFalse-1.00NON AVAILABLE-1.00NON AVAILABLEFalse2.00Pacific Heights94115.00(37.78814050763782, -122.43440743226256)1463876236318
82014041130258otc alterations permit11/04/201437400290.01FolsomSt0.00existing office and storage facility. new partitions, restroom upgrade. electrical on a separate permitcomplete02/10/201411/04/201425/04/201410/02/201425/04/2014False0.030.03FalseFalse09/04/20170.000.00office0.00office0.002.00False3.00constr type 33.00constr type 3False6.00Financial District/South Beach94105.00(37.78992343288329, -122.3915399961996)1338371165676
92017021091348otc alterations permit10/02/201701950010.09MontgomerySt0.00basement & 1/f- as built drawing for fire/smoke damper on hvac duct (existing) under pa#201612074341. n/a ordinance #155-13complete09/03/201710/02/201710/02/201703/09/201710/02/2017False0.060.06FalseFalse05/02/20180.000.00retail sales0.00retail sales0.002.00False3.00constr type 33.00constr type 3False3.00Chinatown94111.00(37.79621627942539, -122.40375479881872)145294162223
Permit NumberPermit TypePermit Type DefinitionPermit Creation DateBlockLotStreet NumberStreet NameStreet SuffixUnitDescriptionCurrent StatusCurrent Status DateFiled DateIssued DateCompleted DateFirst Construction Document DateStructural NotificationNumber of Existing StoriesNumber of Proposed StoriesVoluntary Soft-Story RetrofitFire Only PermitPermit Expiration DateEstimated CostRevised CostExisting UseExisting UnitsProposed UseProposed UnitsPlansetsTIDF ComplianceExisting Construction TypeExisting Construction Type DescriptionProposed Construction TypeProposed Construction Type DescriptionSite PermitSupervisor DistrictNeighborhoods - Analysis BoundariesZipcodeLocationRecord ID
1989002015060277678otc alterations permit02/06/20157101A0070.09HuronAv0.00reroofingcomplete11/03/201602/06/201502/06/201503/11/201602/06/2015False0.040.04FalseFalse27/05/20160.000.001 family dwelling0.001 family dwelling0.000.00False5.00wood frame (5)5.00wood frame (5)False11.00Outer Mission94112.00(37.71192805393076, -122.4500944316179)1383549221502
198901M8710278otc alterations permit28/12/201718700230.1825ThAv0.00street spaceissued28/12/201728/12/201728/12/201701/01/190028/12/2017False0.000.00FalseFalse21/02/20240.000.00Desconocido0.00Desconocido0.00NaNFalse-1.00NON AVAILABLE-1.00NON AVAILABLEFalse4.00Sunset/Parkside94122.00(37.75921574765919, -122.48300451049278)1492023118112
1989022016092081758otc alterations permit20/09/20162130A006G0.01CragmontAv0.00replace (e) tub with walk in tub, 20 amp circuit, gfci outletcomplete28/09/201620/09/201620/09/2016NaN20/09/2016False0.040.04FalseFalse15/09/20170.000.001 family dwelling0.001 family dwelling0.000.00False5.00wood frame (5)5.00wood frame (5)False7.00Inner Sunset94116.00(37.74973733662819, -122.4671301493082)1438114126978
1989032014071614168otc alterations permit16/07/201425280240.01CrestlakeDr0.00cut a door and a staircase for access to backyard.build deck next to staircase.complete23/01/201516/07/201428/10/2014NaN28/10/2014True0.040.04FalseFalse23/10/20150.000.001 family dwelling0.001 family dwelling0.002.00False5.00wood frame (5)5.00wood frame (5)False4.00Sunset/Parkside94132.00(37.73542953855242, -122.48149902038024)1349089138746
198904M6510478otc alterations permit30/12/201561760080.04HarknessAv0.00street spaceissued30/12/201530/12/201530/12/201501/01/190030/12/2015False0.000.00FalseFalse21/02/20240.000.00Desconocido0.00Desconocido0.00NaNFalse-1.00NON AVAILABLE-1.00NON AVAILABLEFalse10.00Visitacion Valley94134.00(37.71765127619624, -122.40342209330782)1407979197645
1989052016040539583additions alterations or repairs05/04/201672950210.3920ThAv0.00ti for space 129,130, 132 partitions, mep, shelving, stonestown mall. ab -56 compliance 2014-0627-9771complete22/11/201605/04/201618/07/2016NaN18/07/2016False0.040.04FalseFalse03/07/20190.000.00retail sales0.00retail sales0.002.00False2.00constr type 22.00constr type 2False7.00Lakeshore94132.00(37.728556952954136, -122.47676641508518)1418495226171
1989062015102708808otc alterations permit27/10/20154009001A0.23MariposaSt0.00install new 2" underground combination fire main. ref app#201402108156issued27/10/201527/10/201527/10/201501/01/190027/10/2015False0.050.05FalseTrue21/10/20160.000.001 family dwelling0.001 family dwelling0.002.00False5.00wood frame (5)5.00wood frame (5)False10.00Potrero Hill94107.00(37.76328445631136, -122.40287014554292)1400885168656
1989072016072937418otc alterations permit29/07/201642620200.16San BrunoAv0.00replace rotten wooden moldings in kind front stairs less than 50% replaceissued29/07/201629/07/201629/07/201601/01/190029/07/2016False0.040.04FalseFalse24/07/20170.000.001 family dwelling0.001 family dwelling0.000.00False5.00wood frame (5)5.00wood frame (5)False10.00Mission94110.00(37.75260530951628, -122.40400191084352)1431897172643
1989082017010666918otc alterations permit06/01/201701460070.08BroadwaySt0.00revision to pa 2016-0926-8773; remove (e) storefront & doors, remove floor, ceiling & wall finishes for new. new storefront, (n) wall & ceiling finishes, (n) flooring with waterproofing in a (n) alcove. mep under pa#201503100377issued01/02/201706/01/201701/02/201701/01/190001/02/2017False0.040.04FalseFalse27/01/20180.000.00retail sales0.00retail sales0.002.00False5.00wood frame (5)5.00wood frame (5)False3.00Chinatown94133.00(37.79800446861674, -122.4080339831039)1449660232064
1989092016042555998otc alterations permit25/04/201637080560.06MarketSt0.0016th floor - (4) evacuation plans.issued25/04/201625/04/201625/04/201601/01/190025/04/2016False0.490.49FalseTrue20/04/20170.000.00office0.00office0.002.00False1.00constr type 11.00constr type 1False6.00Financial District/South Beach94105.00(37.79040639954478, -122.39927546096968)1420790164534